Privacy preserving data mining for social networks

Advances in technology have made it possible to collect personal and professional data about individuals and the connections between them, such as their email correspondence and friendships on the internet. Many agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data. However, in many cases the social network data describes relationships that are private and sharing the data for analysis can result in unacceptable disclosures. Online Social Networks, such as Facebook, are increasingly utilized by many users today. These networks allow users to publish details about themselves and to connect to their friends. Most of the information revealed inside these networks is not private. Yet it is possible to use learning algorithms on released data to predict private information from public information. This paper focuses on the problem of private information leakage from the information present on the social networks. The main topic of the presented effort is the representation of the cause-effect relationships within social network data by the application of the soft computing technique of fuzzy Inference Systems. Also, sanitization techniques that could be used in various situations are suggested and effectiveness of these sanitization techniques is analyzed.